Systems and methods for tracking single-cell movement trajectories

ABSTRACT

A system for tracking single-cell movement trajectories is disclosed. The system can record, to a plurality of frames, cells (events) within a microfluidic device. Also, the system can identify an event within each frame including whether the event is a single cell or multiple cells. When the event appears differently between frames (e.g., single cell in one frame and multiple cells in another frame), the system can either segment or merge the cell(s). Then, the system can determine a trajectory for the events based on a position of the event in the frames. Further, the system can determine cell properties based on the trajectory of the events.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit, under 35 U.S.C. § 119(e), of U.S.Provisional Patent Application No. 62/788,393, filed 4 Jan. 2019, theentire contents and substance of which are incorporated herein byreference in their entirety as if fully set forth below.

STATEMENT REGARDING GOVERNMENT SUPPORT

This invention was made with government support under Grant No. 1552784awarded by the National Science Foundation. The government has certainrights in the invention.

FIELD OF INVENTION

This application is generally related to tracking and sorting cellsaccording to biochemical properties, and, more particularly, to systemsand methods for accurately tracking and sorting single-cell movementtrajectories in microfluidic cell sorting devices.

BACKGROUND

Microfluidics can be used for biological inquiries at the single-celllevel, such as single-cell gene expression for lineage analysis andsignaling dynamics and cell sorting. One application of microfluidicscan be the study of single-cell biomechanical characteristics, such aselasticity, viscosity, stiffness and/or adhesion. Utilizing amicrofluidic channel having ridges that are diagonal with respect to theflow direction, cells can be compressed and translated when passingthrough the channel, and exhibit different trajectories depending ontheir biomechanical properties. The trajectories can also be affected bythe channel design at least based on the ridge height, angle, and/orspacing. The microfluidic approach for studying cellular biomechanicscan be highly cost effective compared to atomic force microscopy, andcan have high throughput similar to flow cytometry. Ridged microfluidicchannels can separate cells at least based on stiffness, size, adhesion,viability, and/or viscoelasticity.

The trajectories can contain rich information pertaining to theinteractions between the cells and the ridged channel, providing anopportunity for quantifying cell biomechanical properties, as well asoptimizing the channel design for various sorting applications. Bymounting the microfluidic chip on an inverted microscope and ahigh-speed camera, cells can be recorded when passing through thechannel, and the trajectories can be computationally extracted from therecordings.

Extracting trajectories from the recordings can appear to be a simpleproblem; however, it can still be challenging to automatically extractthe trajectories with high accuracy. One such challenge arises when onecell collides with another, and the two cells stick together for a whilebefore detaching from each other. This collision and detachment of cellscan be challenging to accurately segment cells in each frame of a videorecording, and thus, consecutive frames must be considered jointly.Another such challenge to accurate trajectory extraction arises whencells move too quickly through the microfluidic channel or when thecontrast between the foreground and the background is low. Existingautomated computational tools for cell tracking and particle trackingare incapable of automatically and accurately tracking the trajectoriesof cells in this application, due to either the collision anddetachment, contrast patterns in the background, or cells withdrastically varying speeds. Accordingly, a need exists for improvedsystems and methods for automatically extracting cell trajectories fromvideo recordings of cells moving through ridged microfluidic channels.

SUMMARY

Aspects of the disclosed technology include systems and methods fortracking single-cell movement trajectories. Consistent with thedisclosed embodiments, the methods can include one or more processors,cameras, microfluidic devices, or databases. One exemplary method caninclude recording a plurality of events (e.g., a single cell, multiplecells, debris, or noise) to a plurality of frames within a ridgedmicrofluidic channel over a predetermined amount of time. The method canalso identify the plurality of events, which can comprise at least onefirst event, at least one second event, and at least one third eventwithin a first frame, a second frame, and a third frame, respectively.The plurality of events can be distinguished from the background basedon each of the plurality of events being beyond a predetermined range ofa background baseline illumination. For each of the plurality of events,the method can determine a center including an x-coordinate and ay-coordinate, a number of pixels, and a radius. Also, the method candetermine a cell type (e.g., a single cell or an aggregate of multiplecells) for each of the plurality of events. Then, the plurality ofevents can be compared to one another to determine whether a matchexists. When each of the plurality of events match one another and are asingle cell, the method can include determining a trajectory of theplurality of events based on the center of each event within the firstframe, the second frame, and the third frame.

Alternately or additionally, when the method determines that the atleast one first event of the first frame is an aggregate of multiplecells, the method can segment the at least one event of the first frame.This can be accomplished by: identifying a plurality of single cells ofthe at least one first event of the first frame; comparing the pluralityof single cells to the at least one event of the second frame todetermine a partial match; and mapping each of the plurality of singlecells to the at least one second event. Mapping can include calculatingan average frame time by dividing the predetermined amount of time by anumber of the plurality events and then determining a probable travelregion (an estimate of a location of the at least one second event) forthe at least one first event based on the average frame time, and thecenter, the number of pixels and/or the radius of the at least one firstevent. Then, the method can determine a trajectory of the plurality ofevents based at least in part on the center of each event within thefirst frame and the second frame.

Alternately or additionally, when the method determines that the atleast one first event of the first frame is a single cell type and thatthe at least one second event of the second frame is an aggregate ofmultiple cells, the method can merge the at least one event of the firstframe and the least one event of the second frame. Merging can beperformed by: identifying a plurality of single cells from the at leastone second event; comparing each of the plurality of single cells to theat least one first event to determine a partial match; and mapping eachof the plurality of single cells to the at least one first event.Mapping can further include: calculating an average frame time bydividing the predetermined amount of time by a number of the pluralityevents; and determining a probable travel region for the at least onefirst event based on the average frame time, and the center, the numberof pixels and/or the radius of the at least one first event. Then, themethod can determine a trajectory of the plurality of events based atleast in part on the center of each event within the first frame and thesecond frame.

In some examples, the method can further involve comparing thetrajectory of the plurality of events to previous trajectories toidentify a most closely matching trajectory of the previoustrajectories. Also, each of the previous trajectories can compriseprevious cell properties. The method can then determine cell propertiesfor the plurality of events based on the previous cell properties of themost closely matching trajectory.

According to some examples, the method can include displaying, by agraphical user interface, the trajectory of the plurality of events.

In some examples, wherein determining the trajectory of the plurality ofevents can be further based on the center of the at least one thirdevent within the third frame.

In some examples, identifying the plurality of events can furtherinclude: determining the amount of illumination of each of the pluralityof frames; determining a background baseline illumination for each ofthe frames based on an average background baseline illumination of apredetermined number of nearby frames; determining an overall frameillumination for each of the frames; and comparing the backgroundbaseline illumination for each of the frames to the respective overallframe illumination to determine that the at least one event is beyond apredetermined range of the background baseline illumination.

Further features of the disclosed design, and the advantages offeredthereby, are explained in greater detail hereinafter with reference tospecific embodiments illustrated in the accompanying drawings, whereinlike elements are indicated be like reference designators.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, are incorporated into and constitute aportion of this disclosure, illustrate various implementations andaspects of the disclosed technology, and, together with the description,serve to explain the principles of the disclosed technology. In thedrawings:

FIG. 1 is a diagram of an example system for tracking single-cellmovement trajectories, in accordance with some examples of the presentdisclosure;

FIG. 2 is an example flow chart of a method for tracking single-cellmovement trajectories, in accordance with some examples of the presentdisclosure;

FIG. 3 is another example flow chart of a method of segmenting multiplecells to single cells to determine single-cell movement trajectories, inaccordance with some examples of the present disclosure; and

FIG. 4 is another example flow chart of a method of merging single cellsto multiple cells to determine single-cell movement trajectories, inaccordance with some examples of the present disclosure.

DETAILED DESCRIPTION

Some implementations of the disclosed technology will be described morefully with reference to the accompanying drawings. This disclosedtechnology can be embodied in many different forms, however, and shouldnot be construed as limited to the implementations set forth herein. Thecomponents described hereinafter as making up various elements of thedisclosed technology are intended to be illustrative and notrestrictive. Many suitable components that would perform the same orsimilar functions as components described herein are intended to beembraced within the scope of the disclosed electronic devices andmethods. Such other components not described herein can include, but arenot limited to, for example, components developed after development ofthe disclosed technology.

It is also to be understood that the mention of one or more method stepsdoes not imply that the methods steps must be performed in a particularorder or preclude the presence of additional method steps or interveningmethod steps between the steps expressly identified.

Reference will now be made in detail to exemplary embodiments of thedisclosed technology, examples of which are illustrated in theaccompanying drawings and disclosed herein. Wherever convenient, thesame references numbers will be used throughout the drawings to refer tothe same or like parts.

FIG. 1 shows an example system 100 that can implement certain methodsfor tracking single-cell movement trajectories. As shown in FIG. 1, insome implementations the system 100 can include computing device 110,which can include one or more processors 112, transceiver 114, graphicaluser interface (GUI) 116, and database 118, among other things. Thesystem 100 can further include microfluidic device 120, light source122, syringe pump 124, outlets 125, camera 126 (e.g., a high-speedcamera), and objective lens 128. The computing device 110 may belong toa research laboratory or another institution involved in, for example,studying cell properties. The system 100 can also include network 130that can include a network of interconnected computing devices such as,for example, an intranet, a cellular network, or the Internet.

The microfluidic device 120 can include a ridged microfluidic channel inwhich a plurality of cells can be inserted via the syringe pump 124. Thedesign of the ridged microfluidic channel which can vary based on ridgeheight, angle, and/or spacing and can be used to separate cells based onstiffness, size, adhesion, viability, and/or viscoelasticity. Cells ofvarying types (e.g., treated cells, untreated cells) can be recorded bycamera 126, using illumination provided by the light source 122, as thecells traverse the microfluidic device 120. Further, the recordings(e.g., plurality of frames of a video file) can be magnified by theobjective lens 128, which can provide as much as 20× magnification. Thecamera 126 can be a high-speed camera that operates at a minimum of 800frames per second with a minimum resolution of 640 by 480 pixels. Aftertraversing through ridges of the microfluidic device 120, cells can exitthe microfluidic device 120 via the outlets 125.

Once the plurality of cells has been recorded, the computing device 110can determine a trajectory of single cells, which can containinformation pertaining to the interactions between the cells and theridged microfluidic channel. Thus, cell biomechanical properties can bequantified and the channel design (ridge microfluidic channel) can beoptimized.

To begin, the computing device 110 can receive the plurality of frames(e.g., video file) from the camera 126 via the transceiver 114. Next,the computing device 110 can identify at least one event (e.g., singlecells, an aggregate of multiple cells, debris, and/or noise) within eachframe. Because the background of each frame is relatively constantwithout any rotation, translation or deformation, and generally has abaseline intensity that varies due to slight changes in theillumination, the background of each frame can be estimated by theaverage of nearby frames. As an example, the one or more processors 112can determine an amount of illumination of each of the plurality offrames, a background baseline illumination for each of the plurality offrames based on an average background baseline illumination of apredetermined number of nearby frames, and an overall frame illuminationfor each of the plurality of frames. Then, the one or more processors112 can compare the background baseline illumination for each of theplurality frames to the respective overall frame illumination todetermine that the at least one event is beyond a predetermined range ofthe background baseline illumination, i.e., distinguishing theforeground objects (e.g., cells) from the background. Thus, a pluralityof events (at least one event for each frame) can be identified, whichcan include a center including an x-coordinate and a y-coordinate, anumber of pixels, a radius, and/or pixel intensity.

The one or more processors 112 can further identify the cell type (e.g.,single cell or an aggregate of multiple cells) based at least in part onthe cell radius and/or the number of pixels of the at least one event.Next, the one or more processors 112 can compare each of the pluralityof events to determine whether a match exists. Determining a match canbe based on comparing the numbers of pixels of each event of a currentframe to identified events of a subsequent frame. Each compared eventcan be scored based on how closely the number of pixels match. Scoringcan also include decrementing the value of an event based on changes ofthe number of pixels between different frames (e.g., between the currentframe and the subsequent frame), distance of movement the event from oneframe to the next frame, and differences in pixel intensity of theevent. Events can be compared in a forward matching manner, i.e.,current frame having an identification of p being compared to asubsequent frame having an identification of value p+1. Also, the eventscan be compared in a backward matching manner, comparing the currentevent having an identification of p to a previous event having anidentification of p−1. Of course, the event having the highest score canbe determined to be a match.

When each of the plurality of events match and are a single cell type,the one or more processors 112 can forego the process of merging andsegmenting the events. In other words, a single cell is identified ineach frame and studying its trajectory is based on the single cell'sposition within each consecutive frame. Thus, the one or more processors112 can determine a trajectory of the plurality of events based on thecenter (position) of the event within each of the plurality of frames.

The events may need to be segmented when, for example, the event of thefirst frame is an aggregate of multiple cells. To accomplish this, theone or more processors 122 can identify a plurality of single cells ofthe second frame. Then, the one or more processors 122 can: calculate anaverage frame time by dividing the total amount of time of the videofile by a number of the plurality frames; determining an average cellspeed, which can be based on historic data of similar cells within asimilar microfluidic device; and determine a probable travel region (anestimate of a location of the second event) for the first event based ona computation using the average cell speed, the average frame time, thecenter, the number of pixels, and/or the radius of the first event.Then, a trajectory of the plurality of events can be determined based atleast in part on the center of each event within the first frame and thesecond frame.

The one or more processors 112 can also merge a single cell to anaggregate of multiple cells. This can occur when the one or moreprocessors 112 determines that the first event is a single cell type,and that the second event is an aggregate of multiple cells. To do so,the one or more processors 112 can: calculate an average frame time;determine average cell speed; and determine a probable travel region forthe first event based on the average frame time, the average cell speed,the center, the number of pixels, and/or the radius of the first event.Next, the one or more processors 112 can identify a plurality of singlecells from the second event within the probable travel region, and thencompare each of the plurality of single cells to the first event todetermine a partial match. Next, the center of the first event can bemapped to the center of the second event, i.e., associating the secondevent's position with the center of the first event. Then, a trajectoryof the plurality of events can be determined based at least in part onthe center of each event within the first frame and the second frame. Ofcourse, the determined trajectory of any of the examples mentioned abovecan be displayed by the GUI 116.

The computing device 110 can also determine cell properties of theidentified events. The computing device 110 can retrieve from memory(e.g., database 118) previous trajectories of previously identifiedevents that can be stored as historic data. In some examples, thecomputing device 110 can receive the historic data from a remote deviceor another database via transceiver 114. The one or more processors 112can compare the trajectory of the plurality of events (e.g., currenttrajectory) to previous trajectories to identify a previous trajectorythat most closely matches the current trajectory. Further, the previoustrajectories data can comprise previous cell properties (e.g.,elasticity, viscosity, stiffness, and/or adhesion). Therefore, the oneor more processors 112 can predict cell properties for the plurality ofevents based on the previous cell properties of the most closelymatching previous trajectory.

FIG. 2 shows an example flow chart of a method for tracking single-cellmovement trajectories. The method 200 is written from the perspective ofthe computing device 110, which can communicate with the camera 126and/or the microfluidic device 120. The computing device 110 can analyzea video file, identify an event within each frame of the video file, anddetermine the trajectory of the event.

At 205, the computing device 110 can analyze a video file that includesa plurality of frames, where each of the frames include an amount ofillumination. Then, at 210, the computing device 110 can calculate atotal amount of time of the video file, which can be used in methods 300and 400 to segment and merge the cells, respectively. At 215, thecomputing device 110 can identify a plurality of events (e.g., at leastone for each of the plurality of frames). As mentioned above,identifying the plurality of events can involve distinguishing thebackground of each frame from the foreground objects. At 220, the methodcan determine a center including an x-coordinate and a y-coordinate, anumber of pixels, and/or a radius for each of the events. Then, at 225,the method can identify the cell type of each of the plurality of events(e.g., single cells or multiple cells). At 230, after determining thateach of the plurality of events are single cells, the computing device110 can compare each of the plurality of events to one another todetermine a match, i.e., that the event of the first frame is the sameas the event of the second frame, etc. At 235, the computing device 110can determine a trajectory of the plurality of events based on thecenter of the event of the first frame, and the center of the event ofthe second frame.

At 240, the computing device 110 can determine that the event of thefirst frame (e.g., first event) is a single cell, which can be based onthe amount of pixels of the event. Similarly, the event of the secondframe (e.g., second event) can be determined to be an aggregate ofmultiple cells, at 245. Then, at 250, the first event can be merged tothe second event, i.e., mapping the position of the single-celled firstevent to the position of the multiple-celled second event, which isfurther described in reference to FIG. 3 below. At 255, a trajectory ofthe plurality of events can be determined based on the center (position)of each event within its respective frame.

At 260, the computing device 110 can determine that the event of thefirst frame is an aggregate of multiple cells and, at 265, the event ofthe second frame can be determined to be a single cell. Then, at 270,the first event can be segmented to the second event, i.e., mapping theposition of the multiple-celled first event to the position of thesingle-celled second event, which is further described in reference toFIG. 4 below. At 275, a trajectory of the plurality of events can bedetermined based on the center (position) of each event within itsrespective frame.

FIG. 3 is an example flow chart of a method of segmenting multiple cellsto determine single-cell movement trajectories. The method 300 can beperformed by the computing device 110 in communication with the camera126, the microfluidic device 120, and/or the objective lens 128. Thecomputing device 110 can identify an aggregate of cells (e.g., multiplecells) in one frame, identify a plurality of single cells comprising theaggregate of cells in the next frame, and then determine a trajectory ofthe single cells.

At 305, the method 300 can include determining that the first event ofthe first frame is an aggregate of multiple cells, which can be based onthe cell size, i.e., the amount of pixels associated with the firstevent being beyond a predetermined cell size range. Therefore, eventswithin the predetermined cell size range can be determined to be singlecells and events beyond the predetermined cell size range can bedetermined to be aggregates of multiple cells. At 310, the computingdevice 110 can identify a plurality of single cells of a second event ofthe second frame. Then, at 315, the computing device 110 can calculatean average frame time of the video file, for example, by dividing thenumber of frames by the total video time. Further, at 320, the computingdevice 110 can determine an average cell speed based on the design ofthe microfluidic device 120. In some examples, determining the averagecell speed can be based on retrieving historic data of previous cells,which can include the average cell speed of previous cells using asimilar or same microfluidic device 120.

At 325, the computing device 110 can then determine a probable travelregion for the first event based on the average cell speed and theaverage frame time. For example, for an average frame time of 0.1 secondand an average cell speed of 0.05 mm/s, the probable travel region wouldbe within 0.005 mm of the center (position) of the first event. Then, at330, the method can include mapping the first event to the plurality ofsingle cells, i.e., associating the center of each of the single cellswith the center of the first event. At 335, the method can thendetermine a trajectory for the plurality of events (e.g., the firstevent and the second event) based on the center of the first event andthe center of the second event.

FIG. 4 is an example flow chart of a method of merging several singlecells to an aggregate of multiple cells to determine single-cellmovement trajectories. Similar to method 300, the method 400 can beperformed by the computing device 110 in communication with the camera126, the microfluidic device 120, and/or the objective lens 128. Thecomputing device 110 can identify single cells of events in one frame,identify multiple cells (another event) in the next frame, and thendetermine a trajectory of the events.

At 405, the computing device 110 determine that the first event of thefirst frame is a single cell type, based on the cell size. Also, at 410,the computing device 110 can determine that the second event of thesecond frame is an aggregate of multiple cells (e.g., multiple cells),based on the cell size. Then, at 415, the computing device 110 cancalculate an average frame time of the video file. Further, at 420, thecomputing device 110 can determine an average cell speed based on thedesign of the microfluidic device 120. At 425, the computing device 110can then determine a probable travel region for the first event based onthe average cell speed and the average frame time. Using the probabletravel region, the computing device 110 can identify a single cell ofthe second event that matches the first event. In other words, thecomputing device 110 can identify a single cell in a first frame, thendetermine that the single cell appears as a multiple cell in a nextframe. Thus, at 430, the computing device 110 can map the second eventto the first event. Finally, at 435, the computing device 110 candetermine a trajectory of the first event and the second event based ontheir position (e.g., center) within the respective frame.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “or” is intended to mean aninclusive “or.” Further, the terms “a,” “an,” and “the” are intended tomean one or more unless specified otherwise or clear from the context tobe directed to a singular form.

In this description, numerous specific details have been set forth. Itis to be understood, however, that implementations of the disclosedtechnology can be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one embodiment,” “an embodiment,” “someembodiments,” “example embodiment,” “various embodiments,” “oneimplementation,” “an implementation,” “example implementation,” “variousimplementations,” “some implementations,” etc., indicate that theimplementation(s) of the disclosed technology so described can include aparticular feature, structure, or characteristic, but not everyimplementation necessarily includes the particular feature, structure,or characteristic. Further, repeated use of the phrase “in oneimplementation” does not necessarily refer to the same implementation,although it can.

As used herein, unless otherwise specified the use of the ordinaladjectives “first,” “second,” “third,” etc., to describe a commonobject, merely indicate that different instances of like objects arebeing referred to, and are not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

While certain implementations of the disclosed technology have beendescribed in connection with what is presently considered to be the mostpractical and various implementations, it is to be understood that thedisclosed technology is not to be limited to the disclosedimplementations, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the scope ofthe appended claims. Although specific terms are employed herein, theyare used in a generic and descriptive sense only and not for purposes oflimitation.

This written description uses examples to disclose certainimplementations of the disclosed technology, including the best mode,and also to enable any person skilled in the art to practice certainimplementations of the disclosed technology, including making and usingany devices or systems and performing any incorporated methods. Thepatentable scope of certain implementations of the disclosed technologyis defined in the claims, and can include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

What is claimed is:
 1. A method for tracking cell movement trajectory,the method comprising: analyzing, with a processor, a plurality offrames of a video file comprising a first frame, a second frame, and athird frame, wherein each of the plurality of frames comprises an amountof illumination; calculating, with the processor, a total amount of timeof the video file; receiving, with a transceiver, historic datacomprising previous event trajectories, previous cell properties,previous cell speeds, and previous numbers of pixels; identifying, withthe processor, a plurality of events, wherein the plurality of eventscomprises an event corresponding to each of the plurality of frames, andwherein each of the plurality of events is a single cell type or anaggregate of multiple cells; determining, for each of the plurality ofevents, cell dimensions comprising a center including an x-coordinateand a y-coordinate, a number of pixels, and a radius; comparing, by theprocessor, the plurality of events; responsive to determining i) a matchbetween each of the plurality of events, and ii) that each of theplurality of events is a single cell type, determining a trajectory ofthe plurality of events based on the cell dimensions of a) a first eventof the first frame, and b) a second event of the second frame.
 2. Themethod of claim 1, further comprising responsive to determining that thefirst event of the first frame is an aggregate of multiple cells,segmenting the first event by: calculating an average frame time bydividing the total amount of time of the video file by a number of theplurality events; determining an average cell speed based on historicdata; determining a probable travel region for the first event withinthe second frame based on i) the average frame time, ii) the averagecell speed, and iii) the cell dimensions of first event, wherein theprobable travel region is an estimate of a location of the second event;identifying a plurality of single cells of the second event based on theprobable travel region; mapping each of the plurality of single cells tothe first event; and determining a trajectory of the plurality of eventsbased at least in part on i) the center of the first event within thefirst frame, and ii) the center of the second event within the secondframe.
 3. The method of claim 1, further comprising responsive todetermining i) that the first event of the first frame is a single celltype, and ii) that the second event of the second frame is an aggregateof multiple cells, merging the first event of the first frame and thesecond event of the second frame by: calculating an average frame timeby dividing the total amount of time of the video file by a number ofthe plurality events; determining an average cell speed based onhistoric data; determining a probable travel region for the first eventwithin the second frame based on i) the average frame time, ii) theaverage cell speed, and iii) the cell dimensions of first event, whereinthe probable travel region is an estimate of a location of the secondevent; mapping the first event to the second event; and determining atrajectory of the plurality of events based at least in part on i) thecenter of the first event within the first frame, and ii) the center ofthe second event within the second frame.
 4. The method of claim 1,further comprising: comparing the trajectory of the plurality of eventsto previous event trajectories to identify a most closely matchingtrajectory of the previous event trajectories, wherein each of theprevious event trajectories is associated with a previous cell property;and determining cell properties for the plurality of events based on theprevious cell property associated with the most closely matchingtrajectory.
 5. The method of claim 1, further comprising: displaying, bya graphical user interface, the trajectory of the plurality of events.6. The method of claim 1, wherein determining the trajectory of theplurality of events is further based on the center of a third eventwithin the third frame.
 7. The method of claim 1, wherein identifyingthe plurality of events further comprises: determining, with theprocessor, the amount of illumination of each of the plurality offrames; determining, with the processor, a background baselineillumination for each of the frames based on an average backgroundbaseline illumination of a predetermined number of nearby frames;determining, with the processor, an overall frame illumination for eachof the frames; and comparing the background baseline illumination foreach of the frames to the respective overall frame illumination todetermine that the at least one event is beyond a predetermined range ofthe background baseline illumination.
 8. A method for tracking cellmovement trajectory, the method comprising: recording, to a plurality offrames with a high-speed camera, a plurality of events within a ridgedmicrofluidic channel over a predetermined amount of time; identifying,with a processor, the plurality of events, the plurality of eventscomprising a first event, a second event, and a third event within afirst frame, a second frame, and a third frame, respectively, based oneach of the plurality of events being beyond a predetermined range of abackground baseline illumination; determining, for each of the pluralityof events, cell dimensions comprising a center including an x-coordinateand a y-coordinate, a number of pixels, and a radius; determining, withthe processor, a cell type for each of the plurality of events, whereinthe cell type is a single cell or an aggregate of multiple cells;comparing, by the processor, each of the plurality of events; responsiveto determining i) a match between each of the plurality of events, andii) that each of the plurality of events is a single cell: determining atrajectory of the plurality of events based on the center of each eventwithin the first frame, the second frame, and the third frame; anddisplaying, by a graphical user interface, the trajectory of theplurality of events; responsive to determining, by the processor, thati) the first event has an aggregate of multiple cells, and ii) thesecond event has a single cell type, segmenting the first event; andresponsive to determining, by the processor, that i) the first event hasa single cell type, and ii) the second event has an aggregate ofmultiple cells, merging the first event to the second event.
 9. Themethod of claim 8, further comprising: comparing the trajectory of theplurality of events to previous event trajectories to identify a mostclosely matching trajectory of the previous event trajectories, whereineach of the previous event trajectories is associated with a previouscell property; and determining cell properties for the plurality ofevents based on the previous cell property associated with the mostclosely matching trajectory.
 10. The method of claim 9, wherein the cellproperties comprise at least one of: elasticity, viscosity, stiffness,or adhesion.
 11. The method of claim 8, wherein the high-speed camerarecords at least 800 frames per second and has a resolution of at least640 by 840 pixels.
 12. The method of claim 8, wherein the segmenting thefirst event comprises: calculating, by the processor, an average frametime by dividing the predetermined amount of time by a number of theplurality events; receiving, with a transceiver, historic datacomprising previous event trajectories, previous cell properties,previous cell speeds, and previous number of pixels; determining anaverage cell speed based on the previous cell speeds; determining aprobable travel region for the first event within the second frame basedon i) the average frame time, ii) the average cell speed, and iii) thecell dimensions of first event, wherein the probable travel region is anestimate of a location of the second event; identifying a plurality ofsingle cells of the second event based on the probable travel region;mapping each of the plurality of single cells to the first event;determining a trajectory of the plurality of events based at least inpart on the center of each event within the first frame and the secondframe; and displaying, by a graphical user interface, the trajectory ofthe plurality of events.
 13. The method of claim 12, further comprising:comparing, by the processor, the second event to the third event todetermine a partial match; mapping, by the processor, the second eventto the third event; and wherein determining the trajectory of theplurality of events is further based on the center of the third eventwithin the third frame.
 14. The method of claim 8, wherein merging thefirst event to the second event comprises: calculating, by theprocessor, an average frame time by dividing the predetermined amount oftime by a number of the plurality events; receiving, with a transceiver,historic data comprising previous event trajectories, previous cellproperties, previous cell speeds, and previous number of pixels;determining an average cell speed based on the previous cell speeds;determining a probable travel region for the first event within thesecond frame based on i) the average frame time, ii) the average cellspeed, and iii) the cell dimensions of first event, wherein the probabletravel region is an estimate of a location of the second event; andmapping the first event to the second event; determining a trajectory ofthe plurality of events based at least in part on the center of eachevent within the first frame and the second frame; and displaying, by agraphical user interface, the trajectory of the plurality of events. 15.The method of claim 14, further comprising: comparing, by the processor,the second event to the third event to determine a partial match;mapping, by the processor, the second event to the third event; andwherein determining the trajectory of the plurality of events is furtherbased on the center of the third event within the third frame.
 16. Asystem for tracking cell movement trajectory, the system comprising: agraphical user interface (GUI); one or more processors; a microfluidicdevice; a high-speed camera; and a memory in communication with the GUI,the one or more processors, the microfluidic device, and the high-speedcamera, storing instructions, that when executed, cause the system to:record, with the high-speed camera, a video file, the video filecomprising a plurality of frames and a plurality of events, theplurality of frames comprising a first frame, a second frame, and athird frame, wherein each of the plurality of events represent one ormore cells within a ridged microfluidic channel of the microfluidicdevice; analyze, with the one or more processors, the plurality offrames, wherein each of the plurality of frames comprise an amount ofillumination; calculate, with the one or more processors, a total amountof time of the video file; receive, with the transceiver, historic datacomprising previous event trajectories, previous cell properties,previous cell speeds, and previous number of pixels; determine, for eachof the plurality of events, cell dimensions comprising a centerincluding an x-coordinate and a y-coordinate, a number of pixels, and aradius; compare, by the one or more processors, the plurality of events;responsive to determining i) a match between each of the plurality ofevents, and ii) that each of the plurality of events is a single celltype, determine a trajectory of the plurality of events based on thecell dimensions of a) a first event of the first frame, and b) a secondevent of the second frame;
 17. The system of claim 16, wherein thememory further stores instructions, that when executed, causes thesystem to, responsive to determining that the first event of the firstframe is an aggregate of multiple cells, segment the first event by:calculating an average frame time by dividing the total amount of timeof the video file by a number of the plurality events; determining anaverage cell speed based on historic data; determining a probable travelregion for the first event within the second frame based on i) theaverage frame time, ii) the average cell speed, and iii) the celldimensions of first event, wherein the probable travel region is anestimate of a location of the second event; identifying a plurality ofsingle cells of the second event based on the probable travel region;mapping each of the plurality of single cells to the first event; anddetermining a trajectory of the plurality of events based at least inpart on i) the center of the first event within the first frame, and ii)the center of the second event within the second frame.
 18. The systemof claim 16, wherein the memory further stores instructions that, whenexecuted, cause the system to: responsive to determining i) that thefirst event of the first frame is a single cell type, and ii) that thesecond event of the second frame is an aggregate of multiple cells,merge the first event of the first frame and the second event of thesecond frame by: calculating an average frame time by dividing the totalamount of time of the video file by a number of the plurality events;determining an average cell speed based on historic data; determining aprobable travel region for the first event within the second frame basedon i) the average frame time, ii) the average cell speed, and iii) thecell dimensions of first event, wherein the probable travel region is anestimate of a location of the second event; mapping the first event tothe second event; and determining a trajectory of the plurality ofevents based at least in part on i) the center of the first event withinthe first frame, and ii) the center of the second event within thesecond frame.
 19. The system of claim 16, further configured to: comparethe trajectory of the plurality of events to previous event trajectoriesto identify a most closely matching trajectory of the previous eventtrajectories, wherein each of the previous event trajectories isassociated with a previous cell property; and determine cell propertiesfor the plurality of events based on the previous cell propertyassociated with the most closely matching trajectory.
 20. The system ofclaim 16, further configured to: display, by the graphical userinterface, the trajectory of the plurality of events.